Output

Data-Cleanup

Missingness

Missigness before removing buffer

plots_missingness

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missingness plots, missing values: 14853 out of 113400 (13.10%)

Missingness after removing buffer

plots_missingness

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missingness plots, missing values: 10227 out of 90720 (11.27%)

pre_filter_missingness

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features filtered, 50.00%, 39 of 630 removed

Missingness after filtering metabolites with more than 50% missing values per group

plots_missingness

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missingness plots, missing values: 6331 out of 85104 (7.44%)

plots_sample_boxplot

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sample boxplot, aes: color=Buffer

plots_sample_boxplot

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sample boxplot, aes: color=tissue

pre_norm_quot

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quotient normalization based on 36 reference samples and 511 variables: ~

Log text:
quotient normalization based on 36 reference samples and 511 variables: Buffer == “80% Meth + 20% H2O”

plots_dilution_factor

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dilution factor plot, ‘Buffer’

plots_sample_boxplot

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sample boxplot, aes: color=Buffer

pre_trans_log

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log2.000000

plots_sample_boxplot

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sample boxplot, aes: color=Buffer

pre_outlier_detection_univariate

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flagged 0 univariate outliers

reporting_data

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Dataset info: 144 samples, 591 features; 25 sample annotation fields, 10 feature annotation fields

write_se_xls

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Data exported to Excel file ‘PreprocessedData.xlsx’

Global Statistics

plots_umap

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UMAP, label_col: , aes: color=Buffer, size=2.5

plots_heatmap

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Heatmap of assay data.

plots_umap

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UMAP, label_col: , aes: color=tissue, size=2.5

plots_heatmap

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Heatmap of assay data.

plots_pca

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PCA, scores, label_col: , aes: color=Buffer, shape=tissue, size=2.5

modify_agg_pathways

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pathway aggregation by ‘Class’, 22 pathway scores generated

Pathway Aggregation Analysis

Buffer analysis

stats_univ_lm

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univariate lm, ~Buffer

post_multtest

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Multiple testing correction of ‘Buffer pw’ using ‘BH’

reporting_stats

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‘Buffer pw’ info

Samples: 50% Meth + 50% H2O: 36, 50% Meth + 50% PBS: 36, 80% Meth + 20% H2O: 36, 80% Meth + 20% PBS: 36 /// features: p.adj < 0.05, 7 of 22

plots_pval_hist

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P-value histograms for Buffer pw

plots_box_scatter

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Feature

Tissue analysis

stats_univ_lm

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univariate lm, ~tissue

post_multtest

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Multiple testing correction of ‘Tissue pw’ using ‘BH’

reporting_stats

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‘Tissue pw’ info

Samples: fat: 48, liver: 48, muscle: 48 /// features: p.adj < 0.05, 21 of 22

plots_pval_hist

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P-value histograms for Tissue pw

plots_box_scatter

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Feature

Multiple Statistics Heatmap

plots_multstats_heatmap

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Pheatmap of stat results. Colors represent (sign(statistic) * -log10(p.adj)))

Result Comparison

plots_stats_compare

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comparison plot between ‘Buffer pw’ and ‘Tissue pw’